caspargross/plasmident repository overview

⁠PlasmIdent

This pipeline idenfitifes circular plasmids in in bacterial genome assemblies using long reads.

It includes the following steps

• Gene prediction with Glimmer3⁠
• Identification of antibiotic resistance genes in the CARD Database RGI⁠
• Long read alignment against assembly
• Coverage analysis with Mosdepth⁠
• GC Content and GC Skew
• Identification of reads that overlap the gap in the plasmid, indicating circular reads

It is created with nextflow, an application to create complex pipelines with repository integration

⁠Requirements

• Linux or Mac OS (Not tested on Windows, might work with docker)
• Java 8.x

⁠Installation

1. Install nextflow⁠

curl -s https://get.nextflow.io | bash 

This creates the nextflow executable in the current directory

1. Download pipeline

You can either get the latest version by cloning this repository

git clone https://github.com/caspargross/plasmident

or download on of the releases⁠.

1. Download dependencies

All the dependencies for this pipeline can be downloaded in a docker⁠ container.

docker pull caspargross/plasmident

Alternative dependency installations:

• Singularity Container⁠
• Use conda environment (no docker)⁠

⁠Run Application

The pipeline requires an input file with a sample id (string) and paths for the assembly file in .fasta format and long reads in .fastq or .fastq.gz. The paths can either be absolute or relative to the launch directory. In normal configuration (with docker), it is not possible to follow symbolic links.

The file must be tab-separated and have the following format

The pipeline is started with the following command:

nextflow run plasmident --input read_locations.tsv

There are other run profiles⁠ for specific environments.

⁠Optional run parameters

• --outDir Path of output folder
• --seqPadding Number of bases added at contig edges to improve long read alignment [Default: 1000]
• --covWindow Moving window size for coverage and gc content calculation [Default: 50]
• --cpu Number of threads used per process
• --targetCov Large read files are subsampled to this target coverage to speed up the process [Default: 50]

⁠Results